6 papers
Next Item Recommendation with Self-Attention
Shuai Zhang, Yi Tay, Lina Yao +1
In this paper, we propose a novel sequence-aware recommendation model. Our model utilizes self-attention mechanism to infer the item-item relationship from user's historical intera…
Expert Recommendation via Tensor Factorization with Regularizing Hierarchical Topical Relationships
Chaoran Huang, Lina Yao, Xianzhi Wang +3
Knowledge acquisition and exchange are generally crucial yet costly for both businesses and individuals, especially when the knowledge concerns various areas. Question Answering Co…
GrCAN: Gradient Boost Convolutional Autoencoder with Neural Decision Forest
Manqing Dong, Lina Yao, Xianzhi Wang +2
Random forest and deep neural network are two schools of effective classification methods in machine learning. While the random forest is robust irrespective of the data domain, th…
Metric Factorization: Recommendation beyond Matrix Factorization
Shuai Zhang, Lina Yao, Yi Tay +3
In the past decade, matrix factorization has been extensively researched and has become one of the most popular techniques for personalized recommendations. Nevertheless, the dot p…
Hybrid Collaborative Recommendation via Semi-AutoEncoder
Shuai Zhang, Lina Yao, Xiwei Xu +2
In this paper, we present a novel structure, Semi-AutoEncoder, based on AutoEncoder. We generalize it into a hybrid collaborative filtering model for rating prediction as well as p…
Dynamic Intention-Aware Recommendation System
Shuai Zhang, Lina Yao
Recommender systems have been actively and extensively studied over past decades. In the meanwhile, the boom of Big Data is driving fundamental changes in the development of recomm…